Items related to Anomaly Detection for Categorical Data
Synopsis
Outlier detection system discovers the novel or rare events, anomalies, vicious actions, exceptional phenomena. It is mandatory to find these anomalies in data mining because the presence of these objects usually makes the database inefficient. An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism. Finding objects that do not conform to well-defined notions of expected behaviour in a dataset is called outlier detection. Outlier detection is a pre-processing step for locating these non-conforming objects in data sets. This outlier detection is a challenging process in large scale database since it has high dimensional data with low anomalous rate. Here outliers are defined formally and the optimized ways to detect outliers is also proposed here. Optimization in outlier detection is achieved by a new concept of holoentropy which combines entropy and total correlation. It is a more effective and efficient practical phenomenon in outlier detection methods. It can be used effectively to deal with both large and high-dimensional datasets.
"synopsis" may belong to another edition of this title.
About the Author
Chiranji Lal Chowdhary working as Assistant Professor (Selection Grade) at VIT Vellore in the School of Information Technology and Engineering. Prior to coming to VIT, he was a Lecturer at the M.S. Ramaiah Institute of Technology, Bangalore.
"About this title" may belong to another edition of this title.
Buy New
View this item
£ 44.45
£ 9.51 shipping from Germany to United Kingdom
Destination, rates & speedsSearch results for Anomaly Detection for Categorical Data
Seller Image
Anomaly Detection for Categorical Data
Published by
LAP LAMBERT Academic Publishing Dez 2017, 2017
ISBN 10: 3330326840
ISBN 13: 9783330326842
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Outlier detection system discovers the novel or rare events, anomalies, vicious actions, exceptional phenomena. It is mandatory to find these anomalies in data mining because the presence of these objects usually makes the database inefficient. An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism. Finding objects that do not conform to well-defined notions of expected behaviour in a dataset is called outlier detection. Outlier detection is a pre-processing step for locating these non-conforming objects in data sets. This outlier detection is a challenging process in large scale database since it has high dimensional data with low anomalous rate. Here outliers are defined formally and the optimized ways to detect outliers is also proposed here. Optimization in outlier detection is achieved by a new concept of holoentropy which combines entropy and total correlation. It is a more effective and efficient practical phenomenon in outlier detection methods. It can be used effectively to deal with both large and high-dimensional datasets. 104 pp. Englisch. Seller Inventory # 9783330326842
Quantity: 2 available
Seller Image
Anomaly Detection for Categorical Data
Published by
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3330326840
ISBN 13: 9783330326842
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Outlier detection system discovers the novel or rare events, anomalies, vicious actions, exceptional phenomena. It is mandatory to find these anomalies in data mining because the presence of these objects usually makes the database inefficient. An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism. Finding objects that do not conform to well-defined notions of expected behaviour in a dataset is called outlier detection. Outlier detection is a pre-processing step for locating these non-conforming objects in data sets. This outlier detection is a challenging process in large scale database since it has high dimensional data with low anomalous rate. Here outliers are defined formally and the optimized ways to detect outliers is also proposed here. Optimization in outlier detection is achieved by a new concept of holoentropy which combines entropy and total correlation. It is a more effective and efficient practical phenomenon in outlier detection methods. It can be used effectively to deal with both large and high-dimensional datasets. Seller Inventory # 9783330326842
Quantity: 1 available
Seller Image
Anomaly Detection for Categorical Data
Published by
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3330326840
ISBN 13: 9783330326842
New
Softcover
Seller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chowdhary Chiranji LalChiranji Lal Chowdhary working as Assistant Professor (Selection Grade) at VIT Vellore in the School of Information Technology and Engineering. Prior to coming to VIT, he was a Lecturer at the M.S. Ramaiah Insti. Seller Inventory # 385708412
Quantity: Over 20 available
Seller Image
Anomaly Detection for Categorical Data
Published by
LAP LAMBERT Academic Publishing Dez 2017, 2017
ISBN 10: 3330326840
ISBN 13: 9783330326842
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -Outlier detection system discovers the novel or rare events, anomalies, vicious actions, exceptional phenomena. It is mandatory to find these anomalies in data mining because the presence of these objects usually makes the database inefficient. An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism. Finding objects that do not conform to well-defined notions of expected behaviour in a dataset is called outlier detection. Outlier detection is a pre-processing step for locating these non-conforming objects in data sets. This outlier detection is a challenging process in large scale database since it has high dimensional data with low anomalous rate. Here outliers are defined formally and the optimized ways to detect outliers is also proposed here. Optimization in outlier detection is achieved by a new concept of holoentropy which combines entropy and total correlation. It is a more effective and efficient practical phenomenon in outlier detection methods. It can be used effectively to deal with both large and high-dimensional datasets.Books on Demand GmbH, Überseering 33, 22297 Hamburg 104 pp. Englisch. Seller Inventory # 9783330326842
Quantity: 2 available
Stock Image
Anomaly Detection for Categorical Data
Published by
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 3330326840
ISBN 13: 9783330326842
New
Paperback
Seller: Revaluation Books, Exeter, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: Brand New. 104 pages. 8.66x5.91x0.24 inches. In Stock. Seller Inventory # 3330326840
Quantity: 1 available